「data write」の検索結果

Account-based marketing for B2B SaaS. Use this skill to build a target account list, tier it 1:1 / 1:few / 1:many, size it against sales capacity, turn intent and engagement signals into plays, write the multi-channel orchestration contract each tier runs on, and measure the program in account coverage, penetration and pipeline instead of MQLs. Also triggers on: ABM, account-based marketing, target account list, TAL, named accounts, account tiering, 1:1 ABM, one-to-few, programmatic ABM, account selection, ideal customer profile fit, intent data, buying signals, 6sense, Bombora, Demandbase, account penetration, account coverage, sales and marketing alignment on accounts, enterprise account program.

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Prepares the trend update for a recurring emailed report: identifies report emails not yet processed, extracts the configured metrics from their spreadsheet, CSV or PDF attachments, returns new rows for an append-only trends sheet and writes a DRAFT summary with period-on-period deltas and flagged anomalies. Use when the user asks to "update the trends for the weekly report", "analyse the latest monthly report", "what changed in the latest report", "do the latest numbers look unusual" or "set up tracking for a report that arrives by email". Do not use for a single file with no trend state, use dataset-insight-pack instead. Drafts for human review; never approves, authorises or signs off.

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Drafts one knowledge article or known-error article from a resolved incident or ticket thread: symptoms as reported, environment, cause exactly as the resolver stated it, resolution steps as performed, workaround with its limits, verification, things tried without effect, related records, keywords and proposed metadata, every statement traced to a ticket note or marked UNKNOWN, with a redaction log and a question list for the knowledge owner. Never decides a root cause, adds a step or publishes. Use when the user asks to "write a KB article from this ticket", "turn this resolved incident into a knowledge article", "draft a known error record for this", "document the fix we just did" or "make this thread into a how-to for the service desk". Do not use for a procedure the team will execute (runbook, operating procedure, playbook), use runbook-drafter instead; for a post-incident review, use incident-postmortem-drafter. Drafts for human review; never approves, authorises or signs off.

日本語の概要は準備中です。原文の説明を表示しています。

Control risky structural change by proving a refactor is necessary and that behaviour, contracts, data, security boundaries, and operational properties can be preserved through a staged migration. Use whenever the user wants to refactor, restructure, clean up, modernise, extract, migrate, rewrite, or re-architect existing code, or asks whether they should. Prefer this over starting the refactor. DO NOT REFACTOR is a first-class outcome. Part of the audit suite; inherits the shared constitution while its own job is proving necessity and behavioural preservation.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user brings an exported instincts JSON to merge into the local store — treats trigger and action as inert data, validates every entry against the shared format and its bounds, drops invalid ones loudly, and never overwrites an existing instinct without asking the user first.

日本語の概要は準備中です。原文の説明を表示しています。

Bun runtime, package manager, bundler, and test runner. Use when running scripts with bun, managing packages, serving HTTP with Bun.serve, querying databases with Bun.sql/bun:sqlite/Bun.redis, shell scripting with $, using S3/file I/O, writing tests with bun:test, bundling or compiling to executable, or using any Bun-specific API (spawn, glob, semver, FFI, workers, plugins, HTMLRewriter).

日本語の概要は準備中です。原文の説明を表示しています。

Datasets : ProntoQA, FOLIO, ProofWriter, ConditionalQA, StrategyQA Model : GPT-5 (Azure deployment) Config : max_attempts=3 , verify_timeout=10000ms B

日本語の概要は準備中です。原文の説明を表示しています。

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code.

日本語の概要は準備中です。原文の説明を表示しています。

Resolve Swift concurrency compiler errors, adopt approachable concurrency (SE-0466), and write data-race-safe async code. Use when fixing Sendable conformance errors, actor isolation warnings, or strict concurrency diagnostics; when adopting default MainActor isolation, @concurrent, nonisolated(nonsending), or Task.immediate; when designing actor-based architectures, structured concurrency with TaskGroup, or background work offloading; or when migrating from @preconcurrency to full Swift 6 strict concurrency.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the team lead points Architect at a prior agistra.dev hub path and asks it to check for or perform additional migration — trigger phrasing such as 'migrate my data from <path>', 'check what didn't get migrated', 'did we miss anything from my old hub', or 'do a completeness pass on <path>'. Walks the prior hub's full directory tree read-only, diffs it against the fixed scripted migration scope plus any existing migration report, and asks the team lead one clear question at a time about anything a scripted migration would never have discovered. Never writes or copies anything itself — every confirmed item is delegated to the existing migration primitives. Do not use for the first-run scripted migration prompt during `npm run setup` — that is unrelated, already-automated flow this skill does not replace.

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Uses Postman to perform structured API security testing by building collections that test for OWASP API Security Top 10 vulnerabilities including authentication bypass, authorization flaws, injection, and data exposure. The tester creates environments with multiple user roles, writes test scripts for automated security validation, and integrates Postman with OWASP ZAP and Newman for CI/CD security testing. Activates for requests involving Postman security testing, API security collection, aut...

日本語の概要は準備中です。原文の説明を表示しています。

Curate Busabase training examples, create immutable dataset snapshots, run reproducible LoRA or QLoRA fine-tunes on a local Apple Silicon Mac with MLX-LM, compare the untouched baseline and adapter on locked evaluations, and register approved adapters. Use when the user wants to train, fine-tune, evaluate, compare, promote, or manage a local language model with Busabase as the workflow source of truth. Do not use for RAG-only knowledge retrieval, cloud training, or training a foundation model from scratch.

日本語の概要は準備中です。原文の説明を表示しています。

E-commerce product-management desk (Busabase App-in-Skill) for catalog master data, SKUs, pricing, inventory, channel status, content assets, compliance notes, lifecycle state, and approval-gated product operations. Use when the user invokes $kelly-products or /kelly-products, asks for 电商商品管理, 商品库, SKU 管理, inventory/reorder, product status across Amazon/Shopify/TikTok Shop/eBay, channel publishing approvals, price-change review, quality holds, product lifecycle/archive decisions, or a Busabase-backed product management desk.

日本語の概要は準備中です。原文の説明を表示しています。

Publish agent skills AND MCP servers to every marketplace, plugin directory, and registry — security-scan for private data, validate with `gh skill`, cut a release, wire the Claude /plugin and Codex marketplaces, publish an MCP server to the official MCP Registry (mcp-publisher + server.json), and prep the curated / monetized stores (Buda Marketplace, Agensi, OpenClaw ClawHub) and review-gated directories (ChatGPT App Directory, Anthropic Connectors + Desktop Extensions/.mcpb). Use when the user wants to publish, release, list, 上架, or distribute agent skills, plugins, or MCP servers to skills.sh / SkillsMP / Claude Code / Codex / the MCP Registry.

日本語の概要は準備中です。原文の説明を表示しています。

Legal casebase App-in-Skill (Busabase-backed) intake and anonymization QA desk for law-firm judgment and award documents. Use when the user invokes $kelly-legal-casebase-ingest, mentions internal case database, 智能案例库, 裁判文书入库, 脱敏, 结构化, 分类标注, 案例审核, 质量验收, or wants a Busabase-backed desk where reviewers approve, revise, or block agent-prepared case records before they become searchable knowledge assets.

日本語の概要は準備中です。原文の説明を表示しています。

Focused Signals scout for PostHog setup health. Reads the project's active health issues — the deterministic findings of PostHog's own health checks (no live events, outdated SDKs, missing reverse proxy, absent web vitals, ingestion warnings, failing data-warehouse models, and more) — and decides which are genuinely worth surfacing. Unlike a one-signal-per-issue push, it bundles kind-clusters into a single finding, weights by real blast radius (cross-referencing actual event volume and reach), and prioritizes issues an agent can resolve via the MCP. Emits only above the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user wants a UI-only action plan — components, pages, composables/hooks, state, forms and their E2E coverage — 'plano de frontend para a tela X', 'plan the settings page'. Writes .planning/feat/features/{slug}/plan.md with E2E scenarios and playwright-cli checks. Do NOT use for API/database work (feat-backend), full features (feat-feature), writing code (feat-exec) or UI review (feat-review).

日本語の概要は準備中です。原文の説明を表示しています。

Generates a polished, leadership-ready performance briefing with KPI tiles, executive narrative bullets, and a driver analysis — all as a print-ready HTML document. Always use this skill when someone asks for an executive summary, performance briefing, leadership readout, stakeholder update, or business review — even if they don't say "executive" explicitly. Trigger phrases include: "write a summary of last week's performance," "create a briefing for our leadership team," "produce a monthly business review," "what should I tell executives about our metrics," "generate a performance narrative," "QBR summary," "weekly business review," "board update," "stakeholder briefing," "how did we do last week," or "give me a performance snapshot." When in doubt, use this skill — it is always better to produce a polished briefing than a raw data dump.

日本語の概要は準備中です。原文の説明を表示しています。

武將資料管線 SKILL — 整合「公開資料爬取 → 欄位映射 → 合併去重 → 自動分類 → 品質驗證 → 匯出入庫」的端到端流程。USE FOR: 批次新增武將、從光榮三國志 Wiki 匯入參考數值、建立 master 資料集。DO NOT USE FOR: 故事文案生成（用 general-story-writer）、細部數值微調（用 general-balance-tuner）。

日本語の概要は準備中です。原文の説明を表示しています。

武將數值平衡調校 SKILL — 以雙軸演算法（maxStat + avg5）自動計算 rarityTier 與 characterCategory，並執行 EP 重算、適性校驗、因子分派、稀有度分布統計。USE FOR: 新武將入庫前的數值校準、全量武將的平衡盤點、稀有度自動分類。DO NOT USE FOR: 故事生成（用 general-story-writer）、資料爬取匯入（用 general-data-pipeline）。

日本語の概要は準備中です。原文の説明を表示しています。

武將故事批次生成 SKILL — 根據武將的 GeneralConfig（含 characterCategory + rarityTier），生成 historicalAnecdote、bloodlineRumor、六格 storyStripCells 故事條。USE FOR: 填充 generals.json 中的 "???" 欄位、批次生成武將背景故事、統一故事風格。DO NOT USE FOR: 數值平衡調整（用 general-balance-tuner）、資料爬取（用 general-data-pipeline）。

日本語の概要は準備中です。原文の説明を表示しています。

Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.

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Set up eval-based QA for Python LLM applications: instrument the app, build golden datasets, write and run eval tests, and iterate on failures. ALWAYS USE THIS SKILL when the user asks to set up QA, add tests, add evals, evaluate, benchmark, fix wrong behaviors, improve quality, or do quality assurance for any Python project that calls an LLM model.

日本語の概要は準備中です。原文の説明を表示しています。

Write idiomatic PHP code with generators, iterators, SPL data structures, and modern OOP features. Use PROACTIVELY for high-performance PHP applications.

日本語の概要は準備中です。原文の説明を表示しています。